willurd

Each of these commands will run an ad hoc http static server in your current (or specified) directory, available at http://localhost:8000. Use this power wisely.

Discussion on reddit.

Python 2.x

$ python -m SimpleHTTPServer 8000

33eyes

Commit jupyter notebooks code to git and keep output locally

1. Add a filter to git config by running the following command in bash inside the repo:

git config filter.strip-notebook-output.clean 'jupyter nbconvert --ClearOutputPreprocessor.enabled=True --to=notebook --stdin --stdout --log-level=ERROR'  

2. Create a .gitattributes file inside the directory with the notebooks

3. Add the following to that file:

mblondel

"""Kernel K-means"""

# Author: Mathieu Blondel <mathieu@mblondel.org>
# License: BSD 3 clause

import numpy as np

from sklearn.base import BaseEstimator, ClusterMixin
from sklearn.metrics.pairwise import pairwise_kernels
from sklearn.utils import check_random_state

mbostock

license: gpl-3.0
redirect: https://observablehq.com/@d3/d3-color-schemes

jeromyanglim

This post examines the features of [R Markdown](http://www.rstudio.org/docs/authoring/using_markdown) 
using [knitr](http://yihui.name/knitr/) in Rstudio 0.96.
This combination of tools provides an exciting improvement in usability for 
[reproducible analysis](http://stats.stackexchange.com/a/15006/183).
Specifically, this post 
(1) discusses getting started with R Markdown and `knitr` in  Rstudio 0.96;
(2) provides a basic example of producing console output and plots using R Markdown;
(3) highlights several code chunk options such as caching and controlling how input and output is displayed;
(4) demonstrates use of standard Markdown notation as well as the extended features of formulas and tables; and 
(5) discusses the implications of R Markdown.

bobthecat

require(inline)
require(RcppArmadillo)

## extract cosine similarity between columns
cosine <- function(x) {
    y <- t(x) %*% x
    res <- 1 - y / (sqrt(diag(y)) %*% t(sqrt(diag(y))))
    return(res)
}

mattismyname

#!/usr/bin/env python
#
# To run, first set this environment variable to point to the Evernote API python bindings you downloaded from Evernote:
#   export PYTHONPATH=/path/to/evernote/python/api
#

import sys
import hashlib
import time
import thrift.protocol.TBinaryProtocol as TBinaryProtocol

dsparks

doInstall <- TRUE
toInstall <- c("alphahull")
if(doInstall){install.packages(toInstall, repos = "http://cran.us.r-project.org")}
lapply(toInstall, library, character.only = TRUE)

# Generate some sample data:
myData <- data.frame(x = rnorm(500), y = rnorm(500))
# With a unit-circle hole in the center:
myData <- myData[sqrt(rowSums(myData^2)) > 1, ]
plot(myData)

simecek

# the code uses 'facebook' function from the previous gist (https://gist.github.com/1634662) or 
# see the original http://romainfrancois.blog.free.fr/index.php?post/2012/01/15/Crawling-facebook-with-R

# scrape the list of friends
friends <- facebook( path="me/friends" , access_token=access_token)
# extract Facebook IDs
friends.id <- sapply(friends$data, function(x) x$id)
# extract names 
friends.name <- sapply(friends$data, function(x)  iconv(x$name,"UTF-8","ASCII//TRANSLIT"))
# short names to initials 

miquelrojascherto

//Demonstrates basic manipulation to predict a EI-MS in Bioclipse using
//a sdf file containing a list of molecule. 
//Requires medea data installed in default location
var mols = cdk.loadMolecules("/Sample Data/SDFfiles/CAS.5.sdf");
for (var i = 0; i < mols.size(); i++) {
	var mol = mols.get(i);
	mol = cdk.generate2dCoordinates(mol);
	ui.open(mol);

	cmlSpectrum = medea.predictMassSpectrum(mol)